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Therefore, FLS transgenic plants containing high flavonol content showed a better in the protection photosynthetic pigments by less reductions of chlorophyll and carotenoid pigments.
Under normal daylight condition, FLS gene expression in WT background didn't show the obvious difference of flavonol contents, comparing to no expression WT (Data not shown).
Moreover, our data also present high relationship between levels of flavonols and photosynthetic pigments with R2 in the range 0.97-0.99.

I.Index Selection and Data Sources A.Index selection In this paper, the industrial emissions intensity index is not doing research.
According to the domestic the research results and the scholars paper data collection and analysis, and finally picked industrial energy consumption, with industrial energy intensity, industrial carbon dioxide emissions,industrial carbon emissions intensity and industrial carbon productivity have different in index analyzing.
B.dData sources 1985 ~ 2007 national industrial energy consumption, industrial added value, can source total consumption data are from the 1985 ~ 2010 China statistical yearbook,the new China 60 years statistics compiled 1949-2007, "China's energy statistics yearbook, after correction finally get papers than required raw data, each year value added of industry in 2005 by calculating the price.
(t check value and F check value are through the show the sexual inspection), through with real data contrast prediction results effective, prediction "fruit such as shown in table 4.
Although Chinese industry has to low carbonization direction, but because of the energy consumption is larger, and according to the results of data processing in nearly 9 years of industrial waste gas emission intensity is still rising stage,2008 just alleviates somewhat, industrial low carbon economic transition pressure is still large, further work is required.

Data agents are responsible for collecting and processing diabetes data from a variety of sources, such as electronic medical records and wearable devices.
The integration of multimodal data, such as genomic, proteomics, and medical imaging data, can be explored to improve diagnostic accuracy.
Result and Discussion Analysis of Discrete Function Model on Diabetes Data.
SVMs, with their ability to map data to high-dimensional spaces, also show good performance.
Machine learning and data mining methods in diabetes research.

The important problem was the reduction of moves considered with each robot model axis in such a way that the virtual model can achieve the required position only in the way that can be achieved by the real robot manipulator.
On the basis of these data the virtual model, in a continuous mode, simulates the real object.
This solution is so universal that it allows connecting any OPC system, including connecting the data sent from the real PLC or robot (allowing visualization in real time the moves of devices or the robot).
For this purpose, the position(.) function allowing recognition of subsequent parts of a string of a TCP / IP text message to the vector position of the axes was elaborated: Private Sub Winsock1_DataArrival(ByVal bytesTotal As Long) Dim I, K, max_data Dim position(20) As String Dim Data_s As String Winsock1.GetData Data, vbString, bytesTotal Data_s = Data K = 0 I = 0 max_data = 6 While (Len(Data_s) >= 1) And (K <= max_data + 1) I = InStr(1, Data_s, " ") If (I > 0) Then position(K) = Trim(Left(Data_s, I)) If (I = 0) Then position(K) = Trim(Data_s) Data_s = Trim(Right(Data_s, Len(Data_s) - I)) K = K + 1 Wend StatusUpdater End Sub Implementation of RS232 communication in VR control application In the case of RS232 serial communication the two solutions of technical data acquisition were possible.
The reading function of cyclic reading of the RS232 port is as follows: Public Sub aktualizuj() Dim z Dim Err As Long Dim cs As COMSTAT Dim ile ClearCommError UserForm1.hCommDev, Err, cs If (cs.cbInQue > 0) Then Data_s=”” ReadFile UserForm1.hCommDev, Buffer_I, z, ile, 0 for Z=0 to ile do Data_s=Data_s+chr(Buffer_I) Next Z End If End Sub After implementing all the necessary functions and constants the test of the whole system was proceeded.

As such these data points have been excluded from the calculation of the line of best fit.
The data point provided by Tafreshi and Thorpe's FE study [11] is also not consistent with the other data.
A method of correction for edge effects, by extrapolating data to the true edge of the hole, was used in Ref. [15] and all thermoelastic data presented in this paper has been edge corrected in this manner.
However, to show the effect of this method of edge correction the raw thermoelastic data is indicated in Fig. 4 by the filled triangle, with the edge corrected data being represented by the filled circle.
Although there is a wide scatter the only data that has been excluded from these calculations is the experimental data of Ellyin [12] and Rau [16] for α = 45º, which clearly does not fit the general trend.

This includes a thorough overview of the pre-processing stage to optimize data for segmentation.
Data Pre-processing In this stage, the focus is placed on data preparation by pre-processing the raw input images to optimize the training results.
For accurate diagnosis, data images must maintain high visual quality with minimal degradation.
This allows the model to detect complex spatial and temporal structures within the data.
Gooding, Data from Lung CT Segmentation Challenge, The Cancer Imaging Archive, 2017

Experimental data have been collected from the literature.
A good agreement has been obtained between experimental data and calculated phase boundaries.
Several data sets are also shown for comparison and the agreement obtained is good.
Accurate experimental data are needed and ab initio estimation can also be used when data are lacking.
Data Monograph No. 9 [8] R.

Gongde Guo et al. [9] suggested a method by constructing a KNN model for the data, which replaces the data to serve as the basis of classification, thus the value of K is automatically determined and is varied for different data.
Step 3 Now, we have two new data sets M and P.
Experiments and Discussion The data set.
The data set is composed of 19 thousands instances with 11 attributes.
Table 1 The description of experimental data set Id The training data size The test data size 1 8000 1000 2 9200 1000 3 10400 1000 4 11600 1000 5 12800 1000 6 14000 1000 7 15200 1000 8 16400 1000 Results and Analysis.

The best fit to the data was obtained with the Langmuir isotherm (non-linear) with maximum monolayer adsorption capacity (Qo) at 5% magnetic loading MBM adsorbent is 0.203 mg/g with Langmuir constants KL and aL are 2.055 L/g and 10.122 L/mg respectively.
The pseudo-first-order (non-linear) kinetic model provides the best correlation of the experimental data with the rate of adsorption (k1) with magnetite loading 2% and 5%, respectively are 0.024 min-1 and 0.022 min-1.
The adsorption equilibrium data obtained from the experiment were examined to find the most fitting model between Langmuir, Freundlich, and BET (Brunauer–Emmett–Teller) using non-linear analysis methods using SSE (Sum of Squared estimate of Errors) values.
Figure 4 illustrates the fitting of experimental kinetic data for Fe (II) adsorption on different magnetite loading (2 and 5% w/w) to the theoretical model.
By examining the experimental data, we acquired that the adsorption rate is affected by the initial concentration of adsorbate and magnetite loading in adsorbents.

Furthermore, an examination of the experimental data was conducted to assess the optimal water activity for the conservation and storage of this plant.
This range of solutions allows for coverage of the entire hygroscopic spectrum and the corresponding water content data [22,23].
Moisture sorption isotherm models were used to analyze data for Marrubium vulgare leaves.
The experimental data of the sorption isotherm are modeled using a third-degree polynomial expression of the equilibrium moisture content.
A third-degree polynomial equation (Eq. 17) is employed to model the experimental data of sorption isotherm curves.